NDLI: Recognition Oriented Facial Image Quality Assessment via Deep Convolutional Neural Network

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Recognition Oriented Facial Image Quality Assessment via Deep Convolutional Neural Network

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Recognition Oriented Facial Image Quality Assessment via Deep Convolutional Neural Network

Content Provider	ACM Digital Library
Author	Xu, Yi Pan, Cenhui Yang, Xiaokang Ni, Bingbing
Abstract	Quality of facial images significantly impacts the performance of face recognition algorithms. Being able to predict "which facial image is good for recognition" is of great importance for real application scenarios, where a sequence of facial images are always presented and one can select "the best quality" image frame for the subsequent matching and recognition task. To this end, we introduce a novel facial image quality automatic assessment framework directly targeting on "selecting better face image for better face recognition". For such as purpose, a deep convolutional neural network (DCNN) is trained to output a general facial quality metric which comprehensively considers various quality factors including brightness, contrast, blurriness, occlusion, pose etc. Based on this trained facial quality metric network, we are able to sort the input face images accordingly and "select" good face images for recognition. Our method is evaluated on the Color FERET and KinectFace face datasets. Results show that the proposed facial image quality metric network well distinguish "good" images from "bad" ones during face recognition.
Starting Page	160
Ending Page	163
Page Count	4
File Format	PDF
ISBN	9781450348508
DOI	10.1145/3007669.3007700
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-08-19
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Convolutional network Face recognition Face selection Face image quality
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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